0) Setup - Data presentation

##        ID             sec            frame            pos_y        
##  10     :  672   Min.   :   52   Min.   :   520   Min.   :  3.561  
##  1      :  671   1st Qu.:  748   1st Qu.:  7480   1st Qu.:136.735  
##  4      :  670   Median : 1649   Median : 16490   Median :286.687  
##  3      :  669   Mean   : 2402   Mean   : 24015   Mean   :255.649  
##  2      :  668   3rd Qu.: 2774   3rd Qu.: 27740   3rd Qu.:387.786  
##  5      :  668   Max.   :13718   Max.   :137180   Max.   :408.962  
##  (Other):12661                                                     
##      pos_x               dY                absdY                cat      
##  Min.   :  19.89   Min.   :-18.97029   Min.   :  0.01638   11     :1576  
##  1st Qu.: 310.80   1st Qu.: -4.71295   1st Qu.:  4.58448   2      :1544  
##  Median : 590.39   Median : -0.95374   Median : 15.05389   5      :1539  
##  Mean   : 616.46   Mean   : -2.33537   Mean   : 26.30399   6      :1537  
##  3rd Qu.: 928.99   3rd Qu.:  0.05349   3rd Qu.: 43.27109   9      :1530  
##  Max.   :1227.18   Max.   : 10.80695   Max.   :169.44433   10     :1528  
##                                                            (Other):7425  
##       gp            ret            ct        pck           expe      
##  Length:16679       1:2412   Min.   :1.000   1:8919   5      :  879  
##  Class :character   2:2479   1st Qu.:3.000   2:5358   15     :  879  
##  Mode  :character   3:2355   Median :4.000   3:2402   1      :  875  
##                     4:2333   Mean   :3.431            7      :  869  
##                     5:2435   3rd Qu.:4.000            11     :  858  
##                     6:4665   Max.   :4.000            16     :  840  
##                                                       (Other):11479  
##      iti            plong       
##  10 min: 1630   Min.   : 12.00  
##  2 min :13572   1st Qu.: 29.00  
##  5 min : 1477   Median : 39.00  
##                 Mean   : 46.28  
##                 3rd Qu.: 48.00  
##                 Max.   :219.00  
## 

1) Figure 2 GAM

GAM

2) Figure 3 RETENTION

Plot

## Stats

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: dY
##      Chisq Df Pr(>Chisq)    
## cat 20.329  2  3.852e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $emmeans
##  cat  emmean   SE   df lower.CL upper.CL
##  1      31.6 2.58 65.0     26.5     36.8
##  10     18.9 2.48 64.2     14.0     23.9
##  Test   18.9 2.64 65.5     13.6     24.1
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10     12.6740 3.21 44.9   3.943  0.0008
##  1 - Test   12.7391 3.33 45.6   3.823  0.0011
##  10 - Test   0.0651 3.26 45.5   0.020  0.9998
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: dY
##      Chisq Df Pr(>Chisq)   
## cat 12.705  2   0.001742 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $emmeans
##  cat  emmean   SE df lower.CL upper.CL
##  1      27.1 2.51 64    22.12     32.1
##  10     17.9 2.51 64    12.92     22.9
##  Test   14.9 2.63 64     9.66     20.2
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10        9.20 3.55 45.9   2.590  0.0336
##  1 - Test     12.21 3.64 47.9   3.354  0.0044
##  10 - Test     3.01 3.64 47.9   0.828  0.6876
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: dY
##     Chisq Df Pr(>Chisq)   
## cat 9.387  2   0.009155 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $emmeans
##  cat  emmean   SE   df lower.CL upper.CL
##  1      31.4 2.83 54.2     25.7     37.0
##  10     21.5 2.97 54.7     15.5     27.4
##  Test   21.7 3.05 54.8     15.6     27.8
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10       9.888 3.77 37.3   2.626  0.0326
##  1 - Test     9.633 3.81 36.5   2.529  0.0411
##  10 - Test   -0.255 3.97 40.1  -0.064  0.9977
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: dY
##      Chisq Df Pr(>Chisq)   
## cat 10.865  2   0.004371 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $emmeans
##  cat  emmean   SE df lower.CL upper.CL
##  1        33 2.70 65     27.6     38.4
##  10       22 3.05 65     15.9     28.1
##  Test     22 2.64 65     16.7     27.3
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10     11.0255 4.08 49.2   2.704  0.0250
##  1 - Test   10.9809 3.78 45.1   2.906  0.0153
##  10 - Test  -0.0445 4.03 46.6  -0.011  0.9999
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: dY
##     Chisq Df Pr(>Chisq)  
## cat 7.867  2    0.01958 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $emmeans
##  cat  emmean   SE   df lower.CL upper.CL
##  1      32.3 2.43 64.8     27.5     37.2
##  10     23.6 2.67 68.0     18.3     28.9
##  Test   30.4 2.57 66.7     25.3     35.5
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10        8.72 3.21 47.3   2.713  0.0247
##  1 - Test      1.94 3.10 44.8   0.624  0.8077
##  10 - Test    -6.78 3.34 49.3  -2.033  0.1149
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

## Analysis of Deviance Table (Type II Wald chisquare tests)
## 
## Response: dY
##      Chisq Df Pr(>Chisq)  
## cat 8.9877  2    0.01118 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## $emmeans
##  cat  emmean   SE df lower.CL upper.CL
##  1      34.8 3.36 42     28.1     41.6
##  10     19.9 3.76 42     12.3     27.5
##  Test   27.6 3.36 42     20.8     34.3
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10       14.92 5.04 30.5   2.957  0.0159
##  1 - Test      7.29 4.76 29.3   1.533  0.2903
##  10 - Test    -7.62 5.04 30.5  -1.511  0.2999
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

3) Figure 4 ITI VARIATION

## [1] "10 min" "2 min"  "5 min"

STATS

STTTR1<-STTT %>% filter(iti=="2 min")
mR1<-lmerTest::lmer(dY~cat+(1|ID),data=STTTR1)
## boundary (singular) fit: see ?isSingular
simR1 <- simulateResiduals(fittedModel = mR1, plot = T)

emmeans(mR1, pairwise ~ cat,adjust="tukey")
## $emmeans
##  cat  emmean   SE df lower.CL upper.CL
##  1      34.8 3.36 42     28.1     41.6
##  10     19.9 3.76 42     12.3     27.5
##  Test   27.6 3.36 42     20.8     34.3
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10       14.92 5.04 30.5   2.957  0.0159
##  1 - Test      7.29 4.76 29.3   1.533  0.2903
##  10 - Test    -7.62 5.04 30.5  -1.511  0.2999
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates
STTTR2<-STTT %>% filter(iti=="5 min")
mR2<-lmerTest::lmer(dY~cat+(1|ID),data=STTTR2)
## boundary (singular) fit: see ?isSingular
simR2 <- simulateResiduals(fittedModel = mR2, plot = T)

emmeans(mR2, pairwise ~ cat,adjust="tukey")
## $emmeans
##  cat  emmean   SE df lower.CL upper.CL
##  1      29.7 3.93 40     21.8     37.7
##  10     23.5 4.11 40     15.2     31.8
##  Test   32.8 3.29 40     26.1     39.4
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10        6.21 5.71 32.5   1.088  0.5279
##  1 - Test     -3.06 5.13 27.8  -0.596  0.8236
##  10 - Test    -9.27 5.27 28.7  -1.759  0.2012
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates
STTTR3<-STTT %>% filter(iti=="10 min")
mR3<-lmerTest::lmer(dY~cat+(1|ID),data=STTTR3)
simR3 <- simulateResiduals(fittedModel = mR3, plot = T)

emmeans(mR3, pairwise ~ cat,adjust="tukey")
## $emmeans
##  cat  emmean   SE   df lower.CL upper.CL
##  1      29.9 3.37 42.7    23.12     36.7
##  10     15.4 3.90 43.0     7.57     23.3
##  Test   29.3 3.49 42.8    22.24     36.3
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  1 - 10       14.50 5.00 31.2   2.902  0.0180
##  1 - Test      0.66 4.66 27.5   0.142  0.9890
##  10 - Test   -13.84 5.08 32.1  -2.723  0.0273
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

4) Supp Figure S1 T1 T2

## [1] "10 min" "2 min"  "5 min"

Stats

mel1<-ST2 %>% filter(iti=="2 min")
mR1<-lmerTest::lmer(dY~cat+(1|ID),data=mel1)
simR1 <- simulateResiduals(fittedModel = mR1, plot = T)

emmeans(mR1, pairwise ~ cat,adjust="tukey")
## $emmeans
##  cat emmean   SE df lower.CL upper.CL
##  1     34.8 3.06 26     28.5     41.1
##  2     21.0 3.61 26     13.6     28.4
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE   df t.ratio p.value
##  1 - 2        13.8 4.66 15.3   2.955  0.0096
## 
## Degrees-of-freedom method: kenward-roger
mel2<-ST2 %>% filter(iti=="5 min")
mR2<-lmerTest::lmer(dY~cat+(1|ID),data=mel2)
## boundary (singular) fit: see ?isSingular
simR2 <- simulateResiduals(fittedModel = mR2, plot = T)

emmeans(mR2, pairwise ~ cat,adjust="tukey")
## $emmeans
##  cat emmean   SE df lower.CL upper.CL
##  1     29.7 3.89 22     21.6     37.8
##  2     24.9 4.29 22     16.0     33.8
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE df t.ratio p.value
##  1 - 2        4.84 5.79 13   0.836  0.4181
## 
## Degrees-of-freedom method: kenward-roger
mel3<-ST2 %>% filter(iti=="10 min")
mR3<-lmerTest::lmer(dY~cat+(1|ID),data=mel3)
simR3 <- simulateResiduals(fittedModel = mR3, plot = T)

emmeans(mR3, pairwise ~ cat,adjust="tukey")
## $emmeans
##  cat emmean   SE df lower.CL upper.CL
##  1     30.1 2.87 29     24.2     36.0
##  2     30.4 3.20 29     23.8     36.9
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE df t.ratio p.value
##  1 - 2      -0.291 4.21 16  -0.069  0.9457
## 
## Degrees-of-freedom method: kenward-roger